class_docstr: Main coreference resolution algorithm
docstr: Clear history, load a list of utterances and an optional context and run the coreference model on them
  
  Arg:
  - `utterances` : iterator or list of string corresponding to successive utterances (in a dialogue) or sentences.
      Can be a single string for non-dialogue text.
  - `utterances_speakers_id=None` : iterator or list of speaker id for each utterance (in the case of a dialogue).
      - if not provided, assume two speakers speaking alternatively.
      - if utterances and utterances_speaker are not of the same length padded with None
  - `context=None` : iterator or list of string corresponding to additionnal utterances/sentences sent prior to `utterances`. Coreferences are not computed for the mentions identified in `context`. The mentions in `context` are only used as possible antecedents to mentions in `uterrance`. Reduce the computations when we are only interested in resolving coreference in the last sentences/utterances.
  - `context_speakers_id=None` : same as `utterances_speakers_id` for `context`. 
  - `speakers_names=None` : dictionnary of list of acceptable speaker names (strings) for speaker_id in `utterances_speakers_id` and `context_speakers_id`
  Return:
      clusters of entities with coreference resolved
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code: def one_shot_coref(
      self,
      utterances,
      utterances_speakers_id=None,
      context=None,
      context_speakers_id=None,
      speakers_names=None,
  ):
      """Clear history, load a list of utterances and an optional context and run the coreference model on them
  
      Arg:
      - `utterances` : iterator or list of string corresponding to successive utterances (in a dialogue) or sentences.
          Can be a single string for non-dialogue text.
      - `utterances_speakers_id=None` : iterator or list of speaker id for each utterance (in the case of a dialogue).
          - if not provided, assume two speakers speaking alternatively.
          - if utterances and utterances_speaker are not of the same length padded with None
      - `context=None` : iterator or list of string corresponding to additionnal utterances/sentences sent prior to `utterances`. Coreferences are not computed for the mentions identified in `context`. The mentions in `context` are only used as possible antecedents to mentions in `uterrance`. Reduce the computations when we are only interested in resolving coreference in the last sentences/utterances.
      - `context_speakers_id=None` : same as `utterances_speakers_id` for `context`.
      - `speakers_names=None` : dictionnary of list of acceptable speaker names (strings) for speaker_id in `utterances_speakers_id` and `context_speakers_id`
      Return:
          clusters of entities with coreference resolved
      """
      self.data.set_utterances(
          context, context_speakers_id, speakers_names
      )
      self.continuous_coref(
          utterances, utterances_speakers_id, speakers_names
      )
      return self.get_clusters()
  
imports: import os
  import spacy
  import numpy
  from neuralcoref.train.utils import PACKAGE_DIRECTORY, SIZE_SINGLE_IN
  from neuralcoref.train.compat import unicode_
  from neuralcoref.train.document import Document, MENTION_TYPE, NO_COREF_LIST
label_desc: Use image node_id 8 for calling the Coref obj's underlying member method code with example usage: obj.one_shot_coref(utterances, utterances_speakers_id, context, context_speakers_id, speakers_names) and returns: self